Resource-efficient record processing in unified automation platforms for robotic process automation

ABSTRACT

Implementations directed to reducing data flow between an AP, and a RPA platform include receiving, by a platform-specific adapter, a set of data records, providing a data change tuple including an identifier, and a hash value, providing a sub-set of data records from the second set of data records based on comparing hash values of data change tuples to a set of stored hash values, the sub-set of data records including fewer data records than the second set of data records, and each data record in the sub-set of data records including a change indicator, determining, by the platform-specific adapter, a tag for each data record in the sub-set of data records based on a respective change indicator, and transmitting a set of messages to the AP, the set of messages communicating tagged data records of the first set of data records, and the sub-set of data records.

BACKGROUND

Robotic process automation (RPA) can be described as the use of softwareto perform high-volume, repeatable tasks on computer systems. Moreparticularly, RPA includes computer software robots (bots) that are eachconfigured to capture and interpret existing applications to, forexample, process a transaction, manipulate data, trigger responses,and/or communicate with other systems. RPA is distinct from automationprocesses in that RPA is aware of, and can adapt to changingcircumstances, exceptions, and new situations. Once an RPA bot has beentrained to capture and interpret the actions of specific processes inexisting software applications, the bot performs its assigned tasksautonomously. In some examples, RPA can expedite back-office andmiddle-office tasks in a wide range of industries, which can include,without limitation, manufacturing, health care, telecom, insurance,finance, procurement, supply chain management (SCM), accounting,customer relationship management (CRM), and human resource management(HRM).

Multiple providers provide RPA services through respective RPAplatforms. As the number of individual RPA platforms, and the number ofbots on respective RPA platforms increase, monitoring, controlling, andmanaging RPA systems become complex, resource-intensive tasks.

SUMMARY

Implementations of the present disclosure are generally directed to aunified automation platform (UAP) for robotic process automation (RPA).More particularly, implementations of the present disclosure aredirected to resource-efficient record processing in UAPs for RPA.

In some implementations, actions include receiving, by aplatform-specific adapter of the AP, a set of data records from a RPAplatform of a plurality of RPA platforms the AP interacts with,providing, by the platform-specific adapter, and from the set of datarecords, a first set of data records including tagged data records, anda second set of data records including untagged data records, for eachdata record in the second set of data records, providing a data changetuple including an identifier, and a hash value, providing a sub-set ofdata records from the second set of data records based on comparing oneor more hash values of data change tuples to a set of stored hashvalues, the sub-set of data records including fewer data records thanthe second set of data records, and each data record in the sub-set ofdata records including a change indicator, determining, by theplatform-specific adapter, a tag for each data record in the sub-set ofdata records based on a respective change indicator, and transmitting,by the platform-specific adapter, a set of messages to the AP, the setof messages communicating tagged data records of the first set of datarecords, and the sub-set of data records. Other implementations of thisaspect include corresponding systems, apparatus, and computer programs,configured to perform the actions of the methods, encoded on computerstorage devices.

These and other implementations can each optionally include one or moreof the following features: if a hash value of a data record does notmatch a hash value of a corresponding stored hashed record, the datarecord is tagged, and is included in the sub-set of data records;comparing hash values of data change tuples to a set of stored hashvalues includes, for at least one data record, determining that theidentifier of the at least one data record is included in the set ofstored hash values; the set of data records is received during a cycle,and the set of stored hash values correspond to data records received inan immediately preceding cycle; hash values are provided using a hashfunction; the platform-specific adaptor is specific to the RPA platform,and the AP communicates with multiple RPA platforms; and a unifiedautomation platform (UAP) includes the AP.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also may include any combination of the aspectsand features provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description, drawings, and claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts an example high-level architecture of an example unifiedautomation platform (UAP) for robotic process automation (RPA).

FIG. 2 depicts an example high-level architecture of an exampleautonomic platform (AP) of the UAP of FIG. 1.

FIG. 3A depicts a schematic diagram illustrating implementations of thepresent disclosure.

FIG. 3B depicts example sets of data records.

FIG. 4 depicts an example process in accordance with implementations ofthe present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Implementations of the present disclosure are generally directed to aunified automation platform (UAP) for robotic process automation (RPA).More particularly, implementations of the present disclosure aredirected to resource-efficient record processing in UAPs for RPA. Asdescribed in further detail herein, resource-efficient processing isachieved using a combined data change tracking, and direct send approachin an adapter of the UAP. In some examples, the adapter facilitatescommunication between a data processing layer of an autonomic platform(AP) of the UAP, and receives transaction records from one or more RPAplatforms. In some examples, the adapter tags incoming records based onrecord content. Any record that is tagged is sent directly to the dataprocessing layer. Any record that is unable to be tagged is provided toa data change tracker (DCT). In some examples, the DCT compares a hashvalue of the record with hash values of previously received records todetermine whether a change has occurred. If a change has occurred, therecord is tagged, and is transmitted to the data processing layer. Thisreduces the burden on technical resources, such as computer memory. Anexample reduction in memory consumption can include approximately 96%over traditional approaches (e.g., approximately 1.8 GB down toapproximately 70 MB) using implementations of the present disclosure.Further, processor consumption can be reduced (e.g., 30% down to 10%).In a UAP, this reduction in memory, and processor consumption ismultiplied across multiple RPA platforms including hundreds, if notthousands of bots.

To provide further context for implementations of the presentdisclosure, RPA can be described as process automation technology thatleverages software-implemented robots (also referred to herein as bots)to perform processes, or portions of processes. In some examples, botsinclude artificial intelligence (AI) features. Example AI featuresinclude, without limitation, intelligent scheduling, computer vision,language detection, entity recognition, and sentiment analysis. An RPAplatform can be provided that includes multiple bots (e.g., tens,hundreds, thousands) executing on hardware systems. In some examples, abot is deployed using a virtual machine (VM) that is executed on ahardware component (e.g., server). In some examples, multiple VMs, eachrunning an instance of a bot, can be deployed on one or more servers.

In some examples, RPA can be implemented in organizations that have manydifferent and complicated systems that need to interact togetherfluidly. For example, when an electronic form from a human resourcesystem is lacking a form field, traditional automation software may flagthe form as having an exception, and an employee would then handle theexception by, for example, looking up the missing information andentering it into the form. Once the form was completed, the employeemight send it on to payroll so that the information could be enteredinto, for example, the organization's payroll system. With RPA, however,and continuing with the above example, a bot can be used, which is ableto adapt, self-learn, and self-correct, handle exceptions, and interactwith the payroll system without human assistance. Furthermore,technologies like presentation-layer automation software—a technologythat mimics the steps of a rules-based, non-subjective process withoutcompromising the existing information technology (IT) architecture—areable to consistently carry out prescribed functions, and scale-up or-down to meet demand.

RPA bots are provided in an RPA platform. Example RPA platforms include,without limitation, Automation Anywhere, Blue Prism, and UiPath. In someexamples, an RPA platform provides a set of tools (e.g., bot developmenttools, bot management tools), libraries, and runtime environments forbots. In some examples, a bot can include one or more data objects, andlogic that encodes a process (or portion of a process) that the bot isto perform. A bot interacts with one or more applications (i.e.,computer-executable programs) to perform one or more jobs (e.g.,processing a set of invoices). In some examples, each job includes oneor more transactions (e.g., processing an invoice of the set ofinvoices), and each transaction can include one or more actions (e.g.,entering invoice information into an application). For example, a dataobject of a bot can be connected to a user interface (UI) of anapplication (e.g., browser-based HTML interfaces, MS Windows interfaces,mainframe terminal interfaces, Java-based interfaces), and the dataobject executes one or more actions using the UI. For example, a dataobject can execute actions to log into an application, enter data,retrieve a result, and log off.

In some examples, a data object includes an application model, and oneor more actions. For example, the application model is specific to anapplication that the bot is to interact with, and exposes elements ofthe UI of the application. The one or more actions include actions thatthe data object can perform with the application.

In some examples, an RPA platform can provide an application server thatfunctions as a common control point for multiple bots, as well as adatabase. In some examples, the database functions as a sharedrepository for the RPA platform, storing code for each bot, work queuesof the bots, audit logs, and the like. An RPA platform can also provideplatform-specific control and monitoring tools for managing bots,creating dashboards, and the like.

Multiple RPA platforms can be provided across multiple enterprises. Forexample, a first RPA platform (e.g., Blue Prism) can be deployed for afirst enterprise, and a second RPM platform (e.g., Automation Anywhere)can be deployed across a second enterprise. As noted above, however,each RPA platform includes platform-specific bots, monitoring, control,and databases. Consequently, each enterprise, and/or third-partyoperating on behalf of enterprises, is required to be knowledgeableabout respective RPA platforms, and implement RPA platform-specificprocesses, and procedures to effectively, and efficiently manage andcontrol bots on the respective RPA platforms.

In accordance with implementations of the present disclosure, a UAP isprovided, which enables control, and monitoring, among otherfunctionality, across multiple, disparate RPA platforms. In this manner,the UAP provides a central platform for management, control, analytics,and the like across multiple RPA platforms, and across multipleenterprises. For example, the UAP can be hosted, or operated by athird-party that performs RPA monitoring and control services formultiple enterprises across multiple, disparate RPA platforms. In someimplementations, and as described in further detail herein, the UAPincludes an RPA operations center (ROC), and an AP. In general, the UAPprovides cross-platform monitoring and control at multiple levels.Example levels include a process level, a bot level, and an RPA platformlevel. The UAP provides, among other functionalities, reporting andanalytics to measure and improve RPA services, and increase RPA levels,as well as control RPA platforms, and individual bots. Accordingly, theUAP of the present disclosure can operate across hundreds, or thousandsof bots across multiple RPA platforms.

FIG. 1 depicts an example UAP 100 in accordance with implementations ofthe present disclosure. The example UAP 100 includes an ROC 102, and anAP 104. In the depicted example, the UAP 100 also includes a master dataentry (MDE) platform 106. In accordance with implementations of thepresent disclosure, the UAP 100 interfaces with one or more RPA systems108 to provide bot monitoring and control, among other functionality,across multiple, disparate RPA platforms, and multiple, disparateenterprises. In some implementations, the UAP 100 communicates with theRPA systems over one or more networks. In some examples, a network caninclude a large computer network, such as a local area network (LAN), awide area network (WAN), the Internet, a cellular network, a telephonenetwork (e.g., PSTN), or any appropriate combination thereof connectingany number of communication devices, mobile computing devices, fixedcomputing devices, and back-end systems.

In some implementations, and as described in further detail herein, theAP 104 provides real-time monitoring, and bot control. As used herein,real-time may describe an operation that is performed without anyintentional delay, taking into account the processing, and/orcommunication limitations of the computing system(s) performing theoperation and the time needed to initiate, and/or perform the operation.Real-time may be used to describe operations that are automaticallyexecuted in response to a triggering event, for example, withoutrequiring human input. In some examples, the AP 104 receives data fromthe RPA systems 108, and processes the data to provide, among otherthings, alerts and events. In some implementations, and as described infurther detail herein, the AP 104 includes interface components (notshown) that provide logic for real-time monitoring and control of botsof the RPA systems 108 (e.g., logic to trigger alerts to support teams).

In some implementations, the ROC 102 provides ex-post reporting,analytics, and visualizations. In some examples, the ROC 102 receivesdata, alerts, events, and the like from the AP 104, and provides datareporting, and analytics across the multiple RPA platforms. For example,the ROC 102 provides UIs (e.g., dashboards) that enables users to viewvisualizations representing performance of RPA platforms, processes,individual bots, and/or groups of bots, across one or more enterprises,for which the RPA platforms are deployed. In some implementations, theUAP 100 enables users to take remedial measures, for example, in theevent that performance is degraded. For example, the user can interactwith the UAP 100 to adjust bot schedules, and/or spin-up, or spin-downbots to address workload fluctuations.

In the depicted example, the ROC 102 includes a database 110, a database(DB) interface 112, a reporting module 114, an analytics module 116, anda data mining module 118. In the depicted example, the AP 104 includes adatabase 120, a data pipeline module 122, messaging components 124, anda ticket handling module 126. In the depicted example, the MDE 106includes a mobilization UI 130, and one or more master data systems(MDSs) 132.

In the examples of FIG. 1, the RPA systems 108 represents multiple RPAplatforms, and/or other bot frameworks that are to be monitored, and/orcontrolled by the UAP 100. In the depicted examples, the RPA systems 108includes multiple RPA servers 140_1, 140_n, each RPA servercorresponding to a respective RPA platform (e.g., RPA server 140_1 is aBlue Prism RPA server; RPA server 140_n is an Automation Anywhereserver). Each RPA server 140_1, 140_n is associated with a respectiveadapter (ADP) 142_1, 142_n. The RPA systems 108 further includeinfrastructure monitoring components 146, one or more other bots 148,and a generic ADP 150.

In accordance with implementations of the present disclosure, the AP 104communicates with the respective RPA servers 140_1, 140_n through thedata pipeline module 122. More particularly, and as described in furtherdetail herein, the data pipeline module 122 ingests data from therespective RPA servers 140_1, 140_n through the respective adapters142_1, 142_n. In some implementations, each adapter 142_1, 142_n isspecific to a respective RPA platform, but is provided using a designpattern, and standardized module across adapters 142_1, 142_n. Theadapters 142_1, 142_n enable communication between the UAP 100, and therespective RPA platforms, manage retrieval of data (e.g., statusinformation) from respective RPA databases, and enable discovery, andcontrol of bots in the respective RPA platforms. Each adapter 142_1,142_n pulls data from the respective RPA platforms 140_1, 140_n

In some implementations, the RPA systems 108 include one or moreapplication program interfaces (APIs) that support communication withthe UAP 100. In some examples, a push-listener API (not shown) isprovided, and enables listening for incoming data that is pushed fromone or more bots. In some examples, the push-listener API receives datafrom bots that are registered with the UAP 100 through the MDE 106.Although the push-listener API may receive data from non-registeredbots, an error message is triggered. In effect, the push-listener API isa corollary to an adapter (e.g., the adapters 142_1, 142_n) for anyautomation tools, and/or bots that are not tied to a particular RPAplatform (e.g., do not have a central automation application database),or where retrieval of monitoring data from such a central database isnot possible. Accordingly, the monitoring data is pushed from individualautomations to the push-listener API, as opposed to being pulled (as isthe case with the adapters 142_1, 142_n).

In some implementations, the generic adapter 150 enables controlling(e.g., starting, stopping) of bots in RPA platforms, for which noplatform-specific adapter (e.g., the adapters 142_1, 142_n) exists. Ingeneral, such bots can be controlled through a command line interface(CLI). The generic adapter 150 calls generic programs, and waits forreturn code. Parties providing such bots implement batch, and executableprograms that are to be called by the generic adapter 150, and provideconfiguration files, and addresses (e.g., uniform resource locators(URLs)) to the batch, and executable programs.

In some implementations, an infrastructure API (not shown) is provided.The infrastructure API can include a web service interface forcommunicating infrastructure monitoring data. In some examples, theinfrastructure API specifies generic data inputs, which infrastructuresprovide, and/or tools can provide. In some implementations, aninfrastructure adapter (not shown) is provided, and can include a set ofscripts (e.g., Powershell scripts) that communicate with theinfrastructure API to monitor infrastructure, for which no othermonitoring tool is available. In this manner, the infrastructure adapterprovides a generic infrastructure monitoring solution.

In some implementations, the MDE 106 enables registration of bots thatare not auto-discovered through adapters (e.g., the adapters 142_1,142_n), or which are not registered directly to the UAP 100 (e.g., by anagent of a provider of the UAP 100). In some examples, the mobilizationUI 130 is a web portal, through which a bot owner/operator can registera bot with the UAP 100. Such owner/operators can include, for example,any person or entity wanting to leverage the functionality provided bythe UAP 100. In some implementations, the mobilization UI 130 validatesdata that is received against so-called golden source systems, andensures completeness of master data of respective bots. In someexamples, the mobilization UI 130 automates onboarding ofnon-discoverable bots, ensures data quality, and avoids non-matchingdata issues. In some examples, the MDS 132 represents any otherthird-party components (e.g., applications, data stores, services thatare not part of the UAP 100), which are used to validate master data aspart of bot onboarding to the UAP 100.

With particular reference to the AP 104, the database 120 is provided asa central database for storing data records received from the RPAsystems 108. In some examples, the database 120 is provided as an onlinetransaction processing (OLTP) database provided on a SQL server. In someimplementations, the data pipeline 122 can include a central messagingsystem that is used to communicate data between components of the UAP100. In some examples, the data pipeline 122 can be provided as amessaging system for communicating voluminous data. In some examples,the data pipeline 122 is provided using Kafka, which is provided by theApache Software Foundation. Kafka can be described as a distributedstreaming platform that enables messaging (e.g., queuing,publish-subscribe) for streaming data records, as well as real-timeprocessing of the data records. In some examples, the data pipeline 122is provided as a cluster (Kafka cluster) on one or more servers, andstores streams of data records, each data record. A data record caninclude a key, a value, and a timestamp. In some examples, the datapipeline 122 receives data records from producers. Example producers caninclude RPA platforms, and bots. In short, the producers produce datarecords, which are received by the data pipeline 122.

In some implementations, the messaging components 124 represent one ormore channels, through which messages can be provided (e.g., to users,to computing systems). Example channels include, without limitation,electronic messages (e-mail), instant messages, text messages, and SMSmessages. For example, logic executed by the AP 104 can trigger an alert(e.g., bot failure), and a message can be sent over one or more of themessaging components 124 in response thereto (e.g., to a userresponsible for monitoring bot activity).

In some implementations, the ticket handling module 126 provides aticket generation and management system that is used to track resolutionof incidents. In some examples, the ticket handling module 126 isprovided by a third-party service provider, and is external to the UAP100. An example ticketing system can be provided by Service Now. In suchexamples, the AP 104 communicates with the external ticketing systemthrough a respective API.

With regard to the ROC 102, data is ingested into the database 110through the database interface 112. In some implementations, thedatabase 110 is provided as an online analytical processing (OLAP)database. In general, the database 110 functions as a central databasefor analytics, reporting, and visualizations. In some examples, thedatabase interface 112 is provided as a database link with OLTP, and/orextraction, transforming, and loading (ETL) (e.g., using SQL ServerIntegration Services (SSIS)). In some examples, the database interface112 receives data from the database 120 of the AP 104, transforms data(e.g., from an OLTP data schema to an OLAP data schema), and performsdata aggregation (e.g., for reporting). In some examples, the databaseinterface 112 receives data from one or more other systems, such as theticket generation and management system introduced above.

In some implementations, the reporting module 114 queries data from thedatabase 110, and provides reports (e.g., textual, graphical). In someexamples, the reporting module 114 provides reports through one or moredashboards, and/or transmits reports to external computing devices(e.g., emails reports to users). In some implementations, the ROC 102includes a reporting API (not shown). In some examples, the reportingAPI exposes a web service that enables data sets to be accessed (e.g.,for consumption by client reporting systems, data mining systems, AIsystems, bespoke reporting systems, analytics systems).

In some implementations, the analytics module 116 provides reporting anddashboard visualizations for RPA support teams (e.g., agents of the UAPresponsible for supporting RPA execution). In some examples, theanalytics module provides access to standardized reports and dashboardsthat provide actionable information to improve RPA execution, andprovide service status, and bot health information. In someimplementations, the data mining module 118 provides more advanceddashboards and data visualizations, and can leverage third-partyservices. An example third-party service can include Tableau provided byTableau Software. In some examples, the ROC 102 includes one or moreother data mining technologies.

As introduced above, implementations of the present disclosure aredirected to resource-efficient record processing in UAPs for RPA. Inparticular, implementations of the present disclosure provideresource-efficient record processing of data records ingested by an AP(e.g., the AP 104 of FIG. 1) from RPA systems (e.g., RPA systems 108 ofFIG. 1).

FIG. 2 depicts an example high-level architecture of an example AP 200of a UAP. For example, the AP 200 of FIG. 2 can be an exampleimplementation of the AP 104 of the UAP 100 of FIG. 1. In the depictedexample, the AP 200 includes a services layer 202, and a data processinglayer 204. In some examples, one or more computing devices 206, 208 cancommunicate with the AP 200 over a network 210. One or more platformadapters 220 are provided, through which the AP 200 communicates withrespective RPA platforms (e.g., Blue Prism, Automation Anywhere,UiPath). In some examples, each adapter 220 is authenticated to itsrespective RPA platform. For example, an adapter 220 includes anauthentication token, which is generated during an installation andauthentication process of the adapter 220 on the respective RPAplatform.

In general, the AP 200 provides detailed status information regardingeach process, and/or resource of respective RPA platforms. Examplestatus information includes, without limitation: how many resources arerunning a process and will be required to complete the assigned tasks(e.g., within a SLA target); how many cases are pending to start, loadednew, and carried over from a previous time period (e.g., day); andexpected completion time, average case time, and oldest case date. Insome examples, a case is a segment of work to be completed by a process.For example, a case can include processing a series of invoices forpayment, a task within the case can include processing a single invoice.The AP 200 also enables control of resources, for example: start andstop resources; restart the automation service the resource is runningon, reboot the resource; a fail-safe prompt to reduce the chance ofhuman error; live information about the resource (e.g., status, lastcase time, and log history); list available resources against all orselected processes with group selection; and providing a calendar forviewing history of processing tasks and logs.

In further detail, the services layer 202 includes a plurality ofservices, through which users can interact with the AP 200. In someexamples, each service is provided as a web service. Example servicesinclude, without limitation, an authentication service 222, aconfiguration service 224, a monitoring service 226, and a controlservice 228.

In some examples, the authentication service 222 authenticates users foraccess to the AP 200. In some examples, authentication is at leastpartially conducted through the network 210 (e.g., the computing device206, 208 connecting through a virtual private network (VPN)). In someexamples, users are explicitly added to the AP through a control panelof the authentication service 222. All user sessions and operations arelogged in a database of the AP 200, described herein. In one example,the authentication service 222 expects valid credentials (e.g.,username, password), and returns an access token that is used insubsequent calls (e.g., when authenticating with a REST API, the AP 200uses a standard OAuth2 Bearer workflow). In some examples, theauthentication service 222 is also responsible for answering queriesabout user permissions to system resources, as well as allowingadministrators to modify the access levels of other users.

In some examples, the configuration service 224 enables configuration ofthe AP 200. Example configurations can include, without limitation,creating/managing user profiles, setting up communication with RPAplatforms, defining polling rates (e.g., frequency of polling RPAplatforms for data), and the like. In some examples, the monitoringservice 226 enables configuration of monitoring activities, andvisualizations to be displayed. For example, the monitoring service 226can be used to identify processes, bots, groups of bots, and/or RPAplatforms that are to be monitored, as well as types of visualizationsto be displayed for each. In some examples, the configuration service224 enables querying, adding, and/or modifying domain-specificconfigurations in the AP 200. Example domain-specific configurationsinclude, without limitation, client information, process information,and resource assignments.

In some implementations, the control service 228 accepts commandrequests for processes and resources, and stores the requests forbackground execution. In some examples, the control service 228 isprovided as a model-view-controller (MVC) service.

In the depicted example, the data processing layer 204 includes adatabase 230, a messaging service 232, an automation data processor 234,an adapter controller 236, and a data pipeline 238. In some examples,the database 230, the messaging service 232, and the data pipeline 238respectively correspond to the database 120, the messaging components124, and the data pipeline 122 of FIG. 1. In some examples, theautomation data processor 234 processes data about the state of the RPAplatforms 260, and ensures that the database 230 reflects informationcorrectly. The automation data processor 234 also calculatesred-amber-green (RAG) statuses of the processes and resources that aremonitored.

In some implementations, multiple automation data processors 234 areprovided, and process data records having respective record types.Example record types are described in further detail herein. Forexample, a first automation data processor 234 is provided, whichprocesses data records of a first type, and a second automation dataprocessor is provided, which processes data records of a second type. Insome examples, and as also described in further detail herein, the datapipeline 238 receives messages with data records from the adapters 220.The data pipeline 238 places the messages in respective processorqueues. For example, messages having data records of the first type areplaced in a first processor queue, and messages having data records ofthe second type are placed in a second processor queue. The automationdata processor 234 retrieves messages from its respective queues, andprocesses the data records as described herein (e.g., determining RAGstatus, inserting into the database 230).

In some implementations, the data processing layer 204 periodicallyreceives data from each RPA platform through respective adapters 220. Insome implementations, each adapter 220 is specific to a respective RPAplatform, and is installed on an RPA server of the RPA platform. Forexample, the adapters 220 of FIG. 2 correspond to the adapters 142_1,142_n of FIG. 1. In some examples, the adapter 220 harmonizes data bytransforming data of the respective RPA platform into a data schema usedby the AP 200, and providing the data to the AP 200 (e.g., to the datapipeline 238). In some examples, each adapter 220 includes multiplemodular libraries, and code packages. One or more libraries are specificto the respective RPA platform that the adapter 220 is installed on. Oneor more libraries are common to all of the adapters 220, regardless ofwhich RPA platform.

In the depicted example, an adapter 220 includes a platform API 250, aplatform data module 252, and a platform interface 254. In someexamples, the platform interface 254 communicates with hardware 258(e.g., servers, on which bots run), and/or a database 256 (e.g., storinglog tables) of the respective RPA platform 260. For example, theplatform interface 254 requests, and receives data from the database256, which data is provided to the data pipeline 238 through theplatform data module 252. In some implementations, the adaptercontroller 236 provides control signals for controlling a process,and/or bot through the platform API 250. For example, a process, and/orbot can be started, or stopped. In some examples, the adapter controller236 can be used to configure, among other things, credentials foraccessing the respective RPA platform 260, setting exception parameters(e.g., to trigger an exception), and the like.

In some implementations, the platform data module 252 relays datarecords (e.g., containing automation information) from the RPA platform260 to the AP 200. Token and SSL authentication is in place to ensurethe data is securely sent. In some examples, the platform data module252 includes an automatic discovery that synchronizes new processes,and/or resources to ensure the AP 200 reflects the current state of theRAP platform 260. In some examples, automation synchronization transfersprocess run, resource run, and truncation status information. In someexamples, the platform data module 252 publishes messages from amessaging queue to an AutomationData topic hosted on the data pipeline238. In some implementations, the platform API 250 receivesauthenticated calls (e.g., REST calls) from the AP 200 to triggerresource action, and/or process action, and/or remote updates ofconfiguration.

As introduced above, the data pipeline 238 can be provided as a Kafkacluster. In some examples, there are three main types of objects in thedata pipeline 238: topics (queues of messages), producers (actors thatadd messages to a topic), and consumers (actors that take messages outof a topic). In the present context, the automation data processor 234is a consumer, and the platform data module 252 is a producer. In someexamples, the data pipeline 238 ensures that consumers of a topicreceive its messages in the order they were added. In the presentcontext, the platform data module 252 produces messages for theAutomationData topic. In some examples, the data in the messagesincludes, without limitation: newly discovered processes, and/orresources in the RPA platform 260; newly created instances of a process,and/or a resource are created; and transaction status, and log data forrunning processes, and/or running resources.

In some implementations, parameters can be defined to determine whethera respective process, bot, and/or RPA platform is operating as expected(e.g., meeting service level agreements (SLAs)), and/or whether thereare any problems (e.g., process unexpectedly stopped, bot performance isdegraded, hardware performance is degraded). In some examples, ifoperation is not executing as expected, and/or there is a problem, anexception is triggered. Example exceptions can include a process, a bot,and/or a RPA platform not meeting a required SLA (e.g., a businessexception), and performance degradation of a process, a bot, and/or aRPA platform (e.g., a technical exception). In response to an exception,reports, and/or alerts can be provided.

In general, the AP 200 ingests data from one or more RPA platformsthrough the data pipeline 238, processes the data using the automationdata processor 234, and stores the data in the database 230. In thedepicted example, a back-up database 240 is provided, which can storeredundant copies of the data, and any reports or the like that the AP200 provides. In some examples, the messaging service 232 transmitsnotifications (e.g., alerts), for example, in response to conditions(e.g., an e-mail to a user responsible for monitoring the particular RPAplatform). In some examples, the reports, and/or alerts are graphicallyrepresented using one or more visualizations (e.g., provided through themonitoring service 226). Example notifications can include, withoutlimitation: process X has completed; resource X has completed; process Xhas a high application exception rate; process X has a high businessexception rate; resource X has stopped responding; and resource X hasnot responded in X minutes.

In some implementations, the data is provided in various data types.Example data types include, without limitation, process run, resourcerun, transaction, log. In some examples, process data is representativeof a particular process executed by a bot (e.g., a process that queuestasks to be performed). In some implementations, data is provided to theAP 200 in one or more data records. For example, a data record having aprocess type can include the example tuple, process identifier, processinformation (e.g., status, bot identifier), timestamp, process run type.In some examples, resource data is representative of a bot performingtasks. For example, a data record having a resource type can include theexample tuple: resource identifier, resource information (e.g., status,process identifier), timestamp, resource run type. In some examples,transaction data is representative of actions (e.g., a task changingfrom a pending status to a running status on a particular bot). Forexample, a data record having a transaction type can include the exampletuple: transaction identifier, transaction status (e.g., pending,running, process identifier, bot identifier), timestamp, transactiontype. In some examples, log data is representative of data added to oneor more logs (e.g., an exceptions log). For example, a data recordhaving a log type can include the example tuple: log identifier, logdata (e.g., application/bot/process failed), timestamp, log type.

In some implementations, each data record is tagged. Example tags caninclude, without limitation, create, update, and delete. A create tagcan indicate that a log entry is to be created, that a process hasstarted, or that a resource has started. An update tag can indicate thatdata associated with a process or a resource has changed. A delete tagcan indicate that a process or a resource has finished.

As introduced above, implementations of the present disclosure reducethe number of data records that are to be processed through a DCT.Further, the DCT processes untagged data records by, for each datarecord, comparing a hash value of the record with hash values ofpreviously received records to determine whether a change has occurred.If a change has occurred, the data record is tagged, and is transmittedto the data processing layer. This reduces the burden on technicalresources, such as computer memory.

FIG. 3A depicts a schematic diagram illustrating implementations of thepresent disclosure. In the example of FIG. 3A, a data extraction module302, a data processor module 304, a message queue 306, and a DCT 308 areprovided. In some examples, the data extraction module 302 receives datarecords from a respective RPA platform (e.g., the RPA platform 260 ofFIG. 2). Example data records include log information recorded by theRPA platform. The log information can be recorded in a significantnumber of log entries (e.g., hundreds, thousands, millions). Forexample, the multiplicity of processes (e.g., one or more automatedactions and activities performed to complete a task), and/or resources(e.g., VMs, processors used to perform work) provided by the RPAplatform results in the relatively high number of data records.

In some implementations, the data extraction module 302, the dataprocessor module 304, the message queue 306, and the DCT 308 areprovided within an adapter (e.g., the adapter 220 of FIG. 2). Forexample, and with reference to the adapter of FIG. 2, the platforminterface 254 provides the data extraction module 302, and the platformdata module 252 provides the data processor module 304, the messagequeue 306, and the DCT 308.

In accordance with implementations of the present disclosure, the dataextraction module 302 receives data records from the RPA platform, andqueues the data records for processing in an extraction queue. In someimplementations, each data record has a corresponding record type. Therecord types can be provided in groups. Example groups includediscovery, and automation. Discovery record types relate to datagenerated in an adaptor discovering new processes, and/or resources ofthe respective RPA platform. Example discovery record types can include,without limitation, queue, process, and resource. Automation recordstypes relate to data generated in automation activities for executingprocesses, and resources. Example automation record types can include,without limitation, process run, resource run, transaction, and log.

The data extraction module 302 processes data records from theextraction queue, and attempts to tag each data record received from theRPA platform. In some implementations, a set of data records is receivedby the data extraction module 302, and the data extraction module 302outputs a first sub-set of data records, and a second sub-set ofdata-records. In some examples, the first sub-set of data recordsincludes tagged data records (i.e., data records of the set of datarecords, for which the data extraction module 302 provided tags). Insome examples, the second sub-set of data records include data records,for which no tags are provided (i.e., data records of the set of datarecords, for which the data extraction module 302 could not providetags). Each data record can include, without limitation, an identifierthat uniquely identifies the data record (e.g., a universally uniqueidentifier (UUI)), data (e.g., type of log (warning, debug), logcontent, resource start/end time, process start/end time), and a recordtype. Accordingly, the data records tagged by the data extraction module302 need not be processed by the DCT 308. In this manner, computingresources and memory that would be otherwise consumed by the DCT 308 areconserved.

In some implementations, the first sub-set of data records, and thesecond sub-set of data records are received by the data processor module304. In some examples, the data processor module 304 provides the firstsub-set of data records as messages 310 that are provided to the messagequeue 306. Among other data of the tagged data records, the messages 310include the respective tag data. In some examples, the data processormodule 304 transforms the tagged data records into respective messagesusing a mapping that maps the tags (e.g., create, update, delete) to arespective message type. In this manner, the data processor module 304transforms the data records from a data schema of the RPA platform to adata schema of the AP (e.g., the AP 200 of FIG. 2) that receives themessages.

In some implementations, the data processor module 304 provides thesecond sub-set of data records to the DCT 308. In some implementations,for each untagged data record, a respective hashed record is provided.In some examples, the hashed record is provided as a tuple including theidentifier (e.g., the identifier of the data record), a hash value, andthe record type. In some examples, the hash value is provided as a hashof content of the data record. The hash value can be provided using aknown hash function (e.g., SHA-256). In some examples, the hashedrecords are provided to the DCT 308.

In some implementations, for each untagged data record, the DCT 308compares a respective hashed record to stored hashed records todetermine whether there is a match. In some examples, the stored hashedrecords correspond to data records received in a previous cycle (e.g.,an immediately preceding cycle), and processed by the DCT 308. A cyclecan include a cycle of extracting data records from the RPA platform(e.g., periodic data extraction). In some examples, each hashed recordincludes an identifier (i.e., the unique identifier assigned to the datarecord), the hash value, and the record type.

In some examples, the identifier (e.g., resource identifier, processidentifier, transaction identifier) of a hashed record is compared toidentifiers in the stored hashed records. If there is a match (i.e., theidentifier is included in the stored hashed records), the hash value iscompared to the stored hash value. If the hash values are different,then a change has occurred since the last cycle (e.g., the content ofthe data record has changed). If a change has occurred, the previouslyuntagged data record corresponding to the identifier is noted with achange (e.g., update), and is added to a set of data records to beoutput by the DCT 308. If the hash values are the same, no change hasoccurred since the last cycle. If no change has occurred, the datarecord corresponding to the identifier is not added to the set of datarecords output by the DCT 308. In some examples, if the identifier ofthe hashed record is not in the stored hashed records, the data recordcorresponding to the identifier is new. Consequently, the data record isnoted with a change (e.g., create), and is added to the set of datarecords to be output by the DCT 308. In some examples, if an identifierof a stored hashed record is not included in the second sub-set of datarecords input to the DCT 308, it is determined that the data recordcorresponding to the identifier has been deleted since the last cycle.Consequently, the data record is tagged (e.g., with a delete tag), andis added to the set of data records to be output by the DCT 308.

The DCT 308 provides a set of data records that have been tagged basedon the identifier, and hash value comparisons. In some examples, the setof data records output by the DCT 308 includes fewer data records thanthe second sub-set of data records, which was input to the DCT 308. Forexample, and as discussed above, data records having unchanged hashvalues are not included in the set of data records output by the DCT308. The data processor 304 receives the set of data records from theDCT 308, and provides corresponding messages for the message queue 306.

FIG. 3B depicts example sets of data records to illustrateimplementations of the present disclosure. A set of data records 350includes data records received from the respective RPA platform. In someexamples, the set of data records 350 includes data records from theextraction queue. Each data record includes a respective identifier(ID), information (INFO), timestamp (T), and record type (TYPE) (e.g.,process run, resource run, transaction, log). Although the set of datarecords 350 includes seven (7) data records, it is contemplated thathundreds, thousands, or millions of data records can be included.

The set of data records 350 is processed to tag one or more records. Forexample, and as described above, the data extraction module 302processes the set of data records to provide a set of data records 352.In the depicted example, the set of data records 352 includes taggeddata records (e.g., ID₁, ID₄, ID₆), and untagged data records (e.g.,ID₂, ID₃, ID₅, ID₇). For example, and as described above, the dataextraction module 302 can tag at least some of the data records toprovide a first sub-set of data records (e.g., ID₁, ID₄, ID₆), and asecond sub-set of data records (e.g., ID₂, ID₃, ID₅, ID₇).

A set of hashed records 354 is provided. For example, the untagged datarecords of the set of data records 352 are processed to provide the setof hashed records 354. In some examples, at least a portion of anuntagged data records is processed through a hash function to provide arespective hash value (HASH). In some examples, the set of hashedrecords 354 is provided by the data processor module 304, and isprovided to the DCT 308.

A set of previous hashed records 356 is provided. In some examples, theset of previous hashed records 356 includes hashed data records that hadbeen evaluated by the DCT 308 in a previous iteration. In some examples,hashed records in the set of hashed records 354 are compared to hashedrecords in the set of previous hashed records 356 to provide an outputset 358. In the example of FIG. 3B, hash values of ID₃ and ID₇ are thesame between the set of hashed records 354, and the set of previoushashed records 356. Consequently, it is determined that those datarecords did not change between iterations, and they are not included inthe output set of hashed records 358. The hash value of ID₂ is differentbetween the set of hashed records 354, and the set of previous hashedrecords 356. Consequently, it is determined that that respectiveprocess, resource, or transaction changed between iterations, and theidentifier is included in the output set 358 with a corresponding changeindicator (e.g., update (U)).

The data record of ID₅ in the set of hashed records 354 is not includedin the set of previous hashed records 356. Consequently, it isdetermined that that respective process, resource, or transaction isnew, and the identifier is included in the output set 358 with acorresponding change indicator (e.g., create (C)). The data record ofID₈ in the set of previous hashed records 356 is not included in the setof hashed records 354. Consequently, it is determined that thatrespective process, resource, or transaction has stopped, and theidentifier is included in the output set 358 with a corresponding changeindicator (e.g., delete (D)). In some examples, the output set 358 isprovided to the data processor module 304, which adds respective tags topreviously untagged data records in the set of data records 352 based onthe respective change indicators from the output set of 358.

As introduced above, the adapter 220 transmits data records to the AP200 through a messaging service (e.g., data records are transferred asmessages). Messages are queued in a message queue within the platformdata module 252. In accordance with implementations of the presentdisclosure, messages (e.g., data records) that are transferred from themessage queue are not immediately deleted from the message queue.Instead, in some implementations, sent messages are only deleted afterconfirmation of receipt has been received from the data processing layer204 of the AP 200. In this manner, there exist concurrent queues toprovide data redundancy for at least a period of time (e.g., until thedata records have been processed by the data pipeline 238. Within thedata processing layer 204, the data records are transferred from thedata pipeline 238, and the automated data processor 234.

In further detail, the message queue of the platform data module 252uses a pointer to keep track of sent messages. For example, if themessage queue includes fifty (50) messages, the pointer is initially setat the first message. If the first ten (10) messages are sent, thepointer moves to the eleventh message. However, the first ten (10)messages remain in the message queue. After the data pipeline 238successfully receives the first ten (10) messages, the data pipeline 238transmits a confirmation for each message. In response to receiving theconfirmation, the platform data module 252 deletes the first (10)messages. Consequently, the eleventh message becomes the first message,to which the pointer point.

FIG. 4 depicts an example process 400 that can be executed inimplementations of the present disclosure. In some examples, the exampleprocess 400 is provided using one or more computer-executable programsexecuted by one or more computing devices.

Data records are received (402). For example, a platform-specificadapter of an AP receives a set of data records from a RPA platform of aplurality of RPA platforms the AP interacts with. In some examples, theplatform-specific adaptor is specific to the RPA platform, and the APcommunicates with multiple RPA platforms. It is determined whether thedata records include any untagged data records (404). For example, theplatform-specific adapter (e.g., a data processor of theplatform-specific adapter) can determine whether any data records areabsent a tag. If there are no untagged data records, messages for alldata records in the set of data records are queued (406), and aretransmitted to the AP (420).

If there are untagged data records, respective sets of data records areprovided for untagged data records, and tagged data records (408). Forexample, the platform-specific adapter provides a first set of datarecords including tagged data records, and a second set of data recordsincluding untagged data records. Messages are queued for any tagged datarecords (410). That is, messages are queued for data records in thefirst set of data records. Hash values of untagged data records areprocessed (412). That is, for example, for each data record in thesecond set of data records, a data change tuple including an identifier,and a hash value is provided. A sub-set of data records is provided(414). For example, a sub-set of data records is provided from thesecond set of data records based on comparing one or more hash values ofdata change tuples to a set of stored hash values. In some examples, thesub-set of data records includes fewer data records than the second setof data records, and each data record in the sub-set of data recordsincludes a change indicator.

Data records of the sub-set of data records are tagged (416). Forexample, the platform-specific adapter determines a tag for each datarecord in the sub-set of data records based on a respective changeindicator. Messages for tagged data records of the sub-set of datarecords are queued (418). A set of messages is transmitted to the AP(420). In some examples, the set of messages communicates tagged datarecords of the first set of data records, and the sub-set of datarecords.

Implementations and all of the functional operations described in thisspecification may be realized in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Implementations may be realized asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium may be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “computing system” encompasses allapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, or multiple processorsor computers. The apparatus may include, in addition to hardware, codethat creates an execution environment for the computer program inquestion (e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or anyappropriate combination of one or more thereof). A propagated signal isan artificially generated signal (e.g., a machine-generated electrical,optical, or electromagnetic signal) that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) may be written in any appropriate form ofprogramming language, including compiled or interpreted languages, andit may be deployed in any appropriate form, including as a stand aloneprogram or as a module, component, subroutine, or other unit suitablefor use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program may bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program may be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry (e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit)).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any appropriate kind of digital computer.Generally, a processor will receive instructions and data from a readonly memory or a random access memory or both. Elements of a computercan include a processor for performing instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive data from ortransfer data to, or both, one or more mass storage devices for storingdata (e.g., magnetic, magneto optical disks, or optical disks). However,a computer need not have such devices. Moreover, a computer may beembedded in another device (e.g., a mobile telephone, a personal digitalassistant (PDA), a mobile audio player, a Global Positioning System(GPS) receiver). Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices (e.g., EPROM, EEPROM, and flash memory devices); magneticdisks (e.g., internal hard disks or removable disks); magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory may besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations may be realizedon a computer having a display device (e.g., a CRT (cathode ray tube),LCD (liquid crystal display) monitor) for displaying information to theuser and a keyboard and a pointing device (e.g., a mouse, a trackball, atouch-pad), by which the user may provide input to the computer. Otherkinds of devices may be used to provide for interaction with a user aswell; for example, feedback provided to the user may be any appropriateform of sensory feedback (e.g., visual feedback, auditory feedback,tactile feedback); and input from the user may be received in anyappropriate form, including acoustic, speech, or tactile input.

Implementations may be realized in a computing system that includes aback end component (e.g., as a data server), a middleware component(e.g., an application server), and/or a front end component (e.g., aclient computer having a graphical user interface or a Web browser,through which a user may interact with an implementation), or anyappropriate combination of one or more such back end, middleware, orfront end components. The components of the system may be interconnectedby any appropriate form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”) and a wide area network (“WAN”), e.g., theInternet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations may also beimplemented in combination in a single implementation. Conversely,various features that are described in the context of a singleimplementation may also be implemented in multiple implementationsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination may in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variation ofa sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemsmay generally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. For example, various formsof the flows shown above may be used, with steps re-ordered, added, orremoved. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A computer-implemented method for reducing dataflow between of an autonomic platform (AP), and one or more roboticprocess automation (RPA) platforms, the method comprising: receiving, bya platform-specific adapter of the AP, a set of data records from a RPAplatform of a plurality of RPA platforms the AP interacts with;providing, by the platform-specific adapter, and from the set of datarecords, a first set of data records comprising tagged data records, anda second set of data records comprising untagged data records; for eachdata record in the second set of data records, providing a data changetuple comprising an identifier, and a hash value; providing a sub-set ofdata records from the second set of data records based on comparing oneor more hash values of data change tuples to a set of stored hashvalues, the sub-set of data records comprising fewer data records thanthe second set of data records, and each data record in the sub-set ofdata records comprising a change indicator; determining, by theplatform-specific adapter, a tag for each data record in the sub-set ofdata records based on a respective change indicator, each tag comprisingone of a create tag, an update tag, and a delete tag, the create tagindicating one of that a log entry is to be created, that a processwithin the RPA platform has started, and that a resource within the RPAplatform has started, the update tag indicating that data associatedwith one of a process and a resource within the RPA platform haschanged, and the delete tag can indicating that one of a process and aresource within the RPA platform has finished; and transmitting, by theplatform-specific adapter, a set of messages to the AP, the set ofmessages communicating tagged data records of the first set of datarecords, and the sub-set of data records.
 2. The method of claim 1,wherein if a hash value of a data record does not match a hash value ofa corresponding stored hashed record, the data record is tagged, and isincluded in the sub-set of data records.
 3. The method of claim 1,wherein comparing hash values of data change tuples to a set of storedhash values comprises, for at least one data record, determining thatthe identifier of the at least one data record is included in the set ofstored hash values.
 4. The method of claim 1, wherein the set of datarecords is received during a cycle, and the set of stored hash valuescorrespond to data records received in an immediately preceding cycle.5. The method of claim 1, wherein hash values are provided using a hashfunction.
 6. The method of claim 1, wherein the platform-specificadaptor is specific to the RPA platform, and the AP communicates withmultiple RPA platforms.
 7. The method of claim 1, wherein a unifiedautomation platform (UAP) comprises the AP.
 8. One or morenon-transitory computer-readable storage media coupled to one or moreprocessors and having instructions stored thereon which, when executedby the one or more processors, cause the one or more processors toperform operations for reducing data flow between of an autonomicplatform (AP), and one or more robotic process automation (RPA)platforms, the operations comprising: receiving, by a platform-specificadapter of the AP, a set of data records from a RPA platform of aplurality of RPA platforms the AP interacts with; providing, by theplatform-specific adapter, and from the set of data records, a first setof data records comprising tagged data records, and a second set of datarecords comprising untagged data records; for each data record in thesecond set of data records, providing a data change tuple comprising anidentifier, and a hash value; providing a sub-set of data records fromthe second set of data records based on comparing one or more hashvalues of data change tuples to a set of stored hash values, the sub-setof data records comprising fewer data records than the second set ofdata records, and each data record in the sub-set of data recordscomprising a change indicator; determining, by the platform-specificadapter, a tag for each data record in the sub-set of data records basedon a respective change indicator, each tag comprising one of a createtag, an update tag, and a delete tag, the create tag indicating one ofthat a log entry is to be created, that a process within the RPAplatform has started, and that a resource within the RPA platform hasstarted, the update tag indicating that data associated with one of aprocess and a resource within the RPA platform has changed, and thedelete tag can indicating that one of a process and a resource withinthe RPA platform has finished; and transmitting, by theplatform-specific adapter, a set of messages to the AP, the set ofmessages communicating tagged data records of the first set of datarecords, and the sub-set of data records.
 9. The computer-readablestorage media of claim 8, wherein if a hash value of a data record doesnot match a hash value of a corresponding stored hashed record, the datarecord is tagged, and is included in the sub-set of data records. 10.The computer-readable storage media of claim 8, wherein comparing hashvalues of data change tuples to a set of stored hash values comprises,for at least one data record, determining that the identifier of the atleast one data record is included in the set of stored hash values. 11.The computer-readable storage media of claim 8, wherein the set of datarecords is received during a cycle, and the set of stored hash valuescorrespond to data records received in an immediately preceding cycle.12. The computer-readable storage media of claim 8, wherein hash valuesare provided using a hash function.
 13. The computer-readable storagemedia of claim 8, wherein the platform-specific adaptor is specific tothe RPA platform, and the AP communicates with multiple RPA platforms.14. The computer-readable storage media of claim 8, wherein a unifiedautomation platform (UAP) comprises the AP.
 15. A system, comprising:one or more processors; and a computer-readable storage device coupledto the one or more processors and having instructions stored thereonwhich, when executed by the one or more processors, cause the one ormore processors to perform operations for reducing data flow between ofan autonomic platform (AP), and one or more robotic process automation(RPA) platforms, the operations comprising: receiving, by aplatform-specific adapter of the AP, a set of data records from a RPAplatform of a plurality of RPA platforms the AP interacts with;providing, by the platform-specific adapter, and from the set of datarecords, a first set of data records comprising tagged data records, anda second set of data records comprising untagged data records; for eachdata record in the second set of data records, providing a data changetuple comprising an identifier, and a hash value; providing a sub-set ofdata records from the second set of data records based on comparing oneor more hash values of data change tuples to a set of stored hashvalues, the sub-set of data records comprising fewer data records thanthe second set of data records, and each data record in the sub-set ofdata records comprising a change indicator; determining, by theplatform-specific adapter, a tag for each data record in the sub-set ofdata records based on a respective change indicator, each tag comprisingone of a create tag, an update tag, and a delete tag, the create tagindicating one of that a log entry is to be created, that a processwithin the RPA platform has started, and that a resource within the RPAplatform has started, the update tag indicating that data associatedwith one of a process and a resource within the RPA platform haschanged, and the delete tag can indicating that one of a process and aresource within the RPA platform has finished; and transmitting, by theplatform-specific adapter, a set of messages to the AP, the set ofmessages communicating tagged data records of the first set of datarecords, and the sub-set of data records.
 16. The system of claim 15,wherein if a hash value of a data record does not match a hash value ofa corresponding stored hashed record, the data record is tagged, and isincluded in the sub-set of data records.
 17. The system of claim 15,wherein comparing hash values of data change tuples to a set of storedhash values comprises, for at least one data record, determining thatthe identifier of the at least one data record is included in the set ofstored hash values.
 18. The system of claim 15, wherein the set of datarecords is received during a cycle, and the set of stored hash valuescorrespond to data records received in an immediately preceding cycle.19. The system of claim 15, wherein hash values are provided using ahash function.
 20. The system of claim 15, wherein the platform-specificadaptor is specific to the RPA platform, and the AP communicates withmultiple RPA platforms.
 21. The system of claim 15, wherein a unifiedautomation platform (UAP) comprises the AP.